def backward(self, x, gy):
# Unpack 1-length tuples
gy, = gy
# Gradient is - x^-T (dx) x^-T
ret = matmul._matmul(-self.invx, gy, transa=True)
ret2 = matmul._matmul(ret, self.invx, transb=True)
return ret2,
def backward(self, x, gy):
# Unpack 1-length tuples
gy, = gy
# Gradient is - x^-T (dx) x^-T
ret = matmul._matmul(-self.invx, gy, transa=True)
ret2 = matmul._matmul(ret, self.invx, transb=True)
return ret2,
def backward(self, x, gy):
a, = x
ga = _matmul(gy[0],
self.b,
transb=not self.transb,
transout=self.transa).reshape(a.shape)
return ga,
def backward(self, x, gy):
if gy[0].ndim == 0:
gx1 = gy[0] * self.value
else:
gx1 = _matmul._matmul(
self.value, gy[0], transa=True, transout=False
).reshape(self._x_shape)
return gx1,
def backward(self, x, gy):
if gy[0].ndim == 0:
gx1 = gy[0] * self.value
else:
gx1 = _matmul._matmul(
self.value, gy[0], transa=True, transout=False
).reshape(self._x_shape)
return gx1,
def backward_cpu(self, x, gy):
a, = x
b = self.b
ga = numpy.empty_like(a)
a0shape = a[0].shape
for i in six.moves.range(len(a)):
ga[i] = _matmul(
gy[0][i], b[i], transb=not self.transb,
transout=self.transa).reshape(a0shape)
return ga,
def forward_cpu(self, x):
a, = x
b = self.b
batch_size = a.shape[0]
shape = self._output_shape(a, b)
ret_dtype = numpy.find_common_type([a.dtype, b.dtype], [])
ret = numpy.empty(shape, dtype=ret_dtype)
for i in six.moves.range(batch_size):
ret[i] = _matmul(
a[i], b[i], transa=self.transa, transb=self.transb)
return ret,
def forward(self, x):
return _matmul._matmul(x[0], self.value),
def forward(self, x):
self.retain_inputs(())
self._x_shape = x[0].shape
return utils.force_array(_matmul._matmul(self.value, x[0])),
def forward(self, x):
self.retain_inputs(())
self._x_shape = x[0].shape
return utils.force_array(_matmul._matmul(self.value, x[0])),
def forward(self, x):
self.retain_inputs(())
self._x_shape = x[0].shape
return _matmul._matmul(self.value, x[0]),
def forward(self, x):
a, = x
return _matmul(a, self.b, transa=self.transa, transb=self.transb),
def backward(self, x, gy):
gx0 = _matmul._matmul(gy[0], self.value, transb=True, transout=False).reshape(x[0].shape)
return (gx0,)
def backward(self, x, gy):
gx1 = _matmul._matmul(
self.value, gy[0], transa=True, transout=False
).reshape(x[0].shape)
return gx1,
def forward(self, x):
return _matmul._matmul(self.value, x[0]),
def backward(self, x, gy):
gx0 = _matmul._matmul(gy[0], self.value, transb=True,
transout=False).reshape(self._x_shape)
return gx0,
def forward(self, x):
self.retain_inputs((0, ))
return utils.force_array(_matmul._matmul(self.value, x[0])),
def forward(self, x):
self.retain_inputs((0,))
return utils.force_array(_matmul._matmul(self.value, x[0])),
def backward(self, x, gy):
gx0 = _matmul(
gy[0], self.b, transb=not self.transb, transout=self.transa
).reshape(x[0].shape)
return gx0,
